Motion detection is an important component of automated video analytics. Generally, it is a first step in initiating object tracking. However, current motion detection techniques are computationally expensive, particularly when applied to real-time applications. Such techniques require parameter fine-tuning to accommodate different patterns of motion, which can in turn, affect the robustness of the result.
This is particularly true in applications involving the stop-and-go flow of traffic where prior art techniques often fail when faced with objects of varying speed. For example, traffic scenarios where objects are moving through a scene at inconsistent or widely varying speeds, including some objects remaining stationary for varying periods of time, can result in the failure of traditional motion detection methods. This is because motion and foreground detection algorithms are typically tuned to support a limited range of speeds of objects moving through the scene.
Stationary objects can be another source of error in prior art techniques because they can be incorrectly categorized as part of the background of the scene. As such, a need exists for improved methods and systems for effective motion detection in video based tracking applications.